linear-regression with torch7 demo - lua

Question

I am following this demo-
https://github.com/torch/demos/blob/master/linear-regression/example-linear-regression.lua
feval = function(x_new)
-- set x to x_new, if differnt
-- (in this simple example, x_new will typically always point to x,
-- so the copy is really useless)
if x ~= x_new then
x:copy(x_new)
end
-- select a new training sample
_nidx_ = (_nidx_ or 0) + 1
if _nidx_ > (#data)[1] then _nidx_ = 1 end
local sample = data[_nidx_]
local target = sample[{ {1} }] -- this funny looking syntax allows
local inputs = sample[{ {2,3} }] -- slicing of arrays.
dl_dx:zero()
local loss_x = criterion:forward(model:forward(inputs), target)
model:backward(inputs, criterion:backward(model.output, target))
return loss_x, dl_dx
end
I have a few doubts in this function
Where is the argument x_new (or its copy x) used in the code?
What does _nidx_ = (_nidx_ or 0) + 1 mean?
what is the value of nidx when the function is first called?
Where is dl_dx updated? Ideally it should have been just after local loss_x is updated, but it isnt written explicitly
EDIT:
My point#4 is very clear now. For those who are interested-
(source- deep learning, oxford, practical 3 lab sheet)

Answer 1

Where is the argument x_new (or its copy x) used in the code?
x is the tensor of parameters of your model. It was previously acquired via x, dl_dx = model:getParameters(). model:forward() and model:backward() automatically use this parameter tensor. x_new is a new set of parameters for your model and is provided by the optimizer (SGD). If it is ever different from your model's parameter tensor, your model's parameters will be set to these new parameters via x:copy(x_new) (in-place copy of tensor's x_new values to x).
What does nidx = (nidx or 0) + 1 mean?
It increases the value of _nidx_ by 1 ((_nidx_) + 1) or sets it to 1 ((0) + 1) if _nidx_ was not yet defined.
what is the value of nidx when the function is first called?
It is never set before that function. Variables which were not yet set have the value nil in lua.
Where is dl_dx updated? Ideally it should have been just after local loss_x is updated, but it isnt written explicitly
dl_dx is the model's tensor of gradients. model:backward() computes the gradient per parameter given a loss and adds it to the model's gradient tensor. As dl_dx is the model's gradient tensor, its values will be increases. Notice that the gradient values are added, which is why you need to call dl_dx:zero() (sets the values of dl_dx in-place to zero), otherwise your gradient values would keep increasing with every call of feval.

Answer 2

x is a global variable, see line 126. The function only seems to update it, not to use it.
This is a common lua idiom: you set things to a parameter or a default value if it is not present. Typical use in functions:
function foo(a, b)
local a = a or 0
local b = b or "foo"
end
The idea is that an expression using and or or evaluates to the first or the second argument, according to the values. x and y yields y if x is not nil or false and x (nil or false) otherwise.
x or y yields y if x is not present (nil or false) and x otherwise. Therefore, or is used for default arguments.
The two can be rewritten the following way:
-- x and y
if x then
return y
else
return x
end
-- x or y
if x then
return x
else
return y
end
you have _nidx_ = (_nidx or 0) + 1, so at the first call of the function, _nidx_ is nil, since it has been defined nowhere. After that, it is (globally) set to 1 (0 + 1)
I'm not sure what you mean exactly. It is reset in line 152 and returned by the function itself. It is a global variable, so maybe there is an outer use for it?